Abstract
Sound recordings are used in various ecological studies, including acoustic wildlife monitoring. Such surveys require automatic detection of target sound events. However, current detectors, especially those relying on band-limited energy, are severely impacted by wind. The rapid dynamics of this noise invalidate standard noise estimators, and no satisfactory method for dealing with it exists in bioacoustics, where simple training and generalization between conditions are important. We propose to estimate the transient noise level by fitting short-term spectrum models to a wavelet packet representation. This estimator is then combined with log-spectral subtraction to stabilize the background level. The resulting adjusted wavelet series can be analysed by standard energy detectors. We use real monitoring data to tune this workflow, and test it on two acoustic surveys of birds. Additionally, we show how the estimator can be incorporated in a denoising method to restore sound. The proposed noise-robust workflow greatly reduced the number of false alarms in the surveys, compared to unadjusted energy detection. As a result, the survey efficiency (precision of the estimated call density) improved for both species. Denoising was also more effective when using the short-term estimate, whereas standard wavelet shrinkage with a constant noise estimate struggled to remove the effects of wind. In contrast to existing methods, the proposed estimator can adjust for transient broadband noises without requiring additional hardware or extensive tuning to each species. It improved the detection workflow based on very little training data, making it particularly attractive for detection of rare species.
Abstract (translated)
URL
https://arxiv.org/abs/2110.05632
